The detection of somatic mutations in primary tumors is critical for the understanding of cancer evolution and targeting therapy. Multiple technologies have been developed to enable the detection of such mutations.
Trang 1R E S E A R C H A R T I C L E Open Access
Use of the QIAGEN GeneReader NGS
system for detection of KRAS mutations,
kit and alternative NGS platform
Agus Darwanto1,7 , Anne-Mette Hein2, Sascha Strauss3, Yi Kong4, Andrew Sheridan1, Dan Richards4, Eric Lader5, Monika Ngowe1,8, Timothy Pelletier1, Danielle Adams1,9, Austin Ricker1, Nishit Patel1, Andreas Kühne3,
Simon Hughes6, Dan Shiffman4, Dirk Zimmermann3, Kai te Kaat3and Thomas Rothmann3*
Abstract
Background: The detection of somatic mutations in primary tumors is critical for the understanding of cancer evolution and targeting therapy Multiple technologies have been developed to enable the detection of such mutations Next generation sequencing (NGS) is a new platform that is gradually becoming the technology of choice for genotyping cancer samples, owing to its ability to simultaneously interrogate many genomic loci at massively high efficiency and increasingly lower cost However, multiple barriers still exist for its broader adoption
in clinical research practice, such as fragmented workflow and complex bioinformatics analysis and interpretation Methods: We performed validation of the QIAGEN GeneReader NGS System using the QIAact Actionable Insights Tumor Panel, focusing on clinically meaningful mutations by using DNA extracted from formalin-fixed paraffin-embedded (FFPE) colorectal tissue with known KRAS mutations The performance of the GeneReader was evaluated and compared to data generated from alternative technologies (PCR and pyrosequencing) as well as an alternative NGS platform The results were further confirmed with Sanger sequencing
Results: The data generated from the GeneReader achieved 100% concordance with reference technologies
Furthermore, the GeneReader workflow provides a truly integrated workflow, eliminating artifacts resulting from
routine sample preparation; and providing up-to-date interpretation of test results
Conclusion: The GeneReader NGS system offers an effective and efficient method to identify somatic (KRAS) cancer mutations
Keywords: GeneReader, Kras, Mutation, Cancer, Ngs
Background
Somatic mutations in the KRAS oncogene are common in
human cancers They are found in 70-90% of pancreatic
cancers [1, 2], 30-50% of colorectal cancers [3–5] and
Several methods have been developed for the detection of
KRAS mutations, each with specific advantages and
limitations [5, 9, 10]
Sanger sequencing has been the ‘gold standard’ for mutation analysis in cancer detection since the 1970s [11] However, limited by its low sensitivity (10-20% mutant allele frequency (MAF)) and low throughput [10], Sanger sequencing is no longer sufficient for the needs of today’s cancer molecular diagnostics
qPCR-based assay used to detect the most common KRAS mutations including those in codons 12 and 13 It has greatly improved sensitivity over Sanger sequencing, and has been approved by the Food and Drug Administration (FDA) [9] for colorectal cancer patient stratification
* Correspondence: Thomas.rothmann@qiagen.com
3 QIAGEN GmbH, QIAGEN Strasse 1, 40724 Hilden, Nordrhein-Westfalen,
Germany
Full list of author information is available at the end of the article
© The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made The Creative Commons Public Domain Dedication waiver
Trang 2Pyrosequencing also offers an attractive alternative to
Sanger due to its fast turnaround time (TAT) and lower
sensitivity threshold, even in tissues with low tumor cell
content [5]
Next-generation sequencing (NGS) differs radically
from the above mentioned methods Coupled with
amplicon-based targeting technology, NGS has the
cap-ability to simultaneously sequence in a massively parallel
way multiple genetic loci with minimal amounts of
nu-cleic acid input and limited time and expense [12–15]
This technology has revolutionized the speed of genetic
and genomic discovery, and advanced our understanding
of molecular mechanisms of diseases In recent years,
NGS has played an important role in advancing
person-alized healthcare and precision medicine by enabling the
identification of mutations associated with therapeutic
response or resistance As more clinically significant
genetic biomarkers and targeted therapies become
avai-lable, the profiling of such genetic variations is becoming
increasingly more critical Several NGS platforms are
already commercially available for sequencing and
iden-tification of genetic alterations associated with diseases,
such as point mutations, deletions, insertions and copy
number variants [16] However, QIAGEN’s GeneReader
System presented here includes all upstream sample
processing steps starting from nucleic acid extraction,
together with an integrated downstream bioinformatics
solution that enables a direct access to real-time updates
from the rapidly evolving literature, and clinical knowledge
and evidence
To this end, we recently evaluated the QIAGEN
GeneReader System workflow from DNA extraction and
purification from FFPE tissue samples, to library
prepa-ration, sequencing and data analysis and interpretation
Herein we show that the GeneReader presents a unified
workflow that provides accurate results and a simple
solution for any laboratory to use in clinical research
Methods
Sample and DNA isolation
FFPE Tumor material from colorectal cancer tumors
(Origene Technologies, MD, USA and Asterand
Biosci-ences, MI, USA) was used to prepare 56 DNA samples
with known KRAS mutation status, previously determined
using therascreen assay (Pyrosequencing and PCR) and
Sanger sequencing according to methods further described
below Tissue sections of 10μm in thickness, ranging from
3 to 20 years of age were used for DNA extraction utilizing
either: i) the QIAamp DNA FFPE Tissue Kit (QIAGEN,
Hilden, Germany) or ii) the GeneRead FFPE DNA Kit
(QIAGEN, Hilden, Germany) according to manufacturer’s
instructions DNA concentration was determined using
the Nanodrop System (Thermo Fisher Scientific, MA,
USA) and Qubit dsDNA HS assay (Life Technologies,
Gaithersburg, USA) The DNA was assessed using the GeneRead DNA QuantiMIZE System (QIAGEN, Hilden, Germany) which utilizes a qPCR-based approach to deter-mine the quality of sample DNA prior to NGS Further-more, both NA12878 (Coriell Institute for Medical Research) (for which the Genome in the Bottle (GIAB) consortium has published a set of high confident vari-ants [17]) and AcroMetrix (Thermo Fisher Scientific,
MA, USA) samples were used as a gold standard set of variant calls
GeneReader sample preparation and sequencing run
In total, 40 ng of DNA measured by Qubit (Thermo Fisher Scientific, MA, USA) was used as template to generate libraries for sequencing Libraries were pre-pared using the QIAGEN Library Kit v2.0 and the GeneRead QIAact Actionable Insight Tumor Panel (QIAGEN, Hilden, Germany), which amplifies 330 amplicons covering 16.7 kb, containing 773 unique variant positions in 12 genes (KRAS, NRAS, KIT, BRAF, PDGFRA, ALK, EGFR, ERBB2, PIK3CA, ERBB3, ESR1 and RAF1) All steps of library preparation were per-formed according to the manufacturer’s protocol The libraries were then quantified using a Qubit dsDNA HS Assay Kit (Life Technologies, MA, USA) and QIAxcel (QIAGEN, Hilden, Germany) Ten individual libraries were pooled prior to emulsion PCR and bead enrich-ment steps that were carried out using an automated protocol on the GeneRead QIAcube (QIAGEN, Hilden, Germany) using the GeneRead Clonal Amp Q Kit (QIAGEN, Hilden, Germany), according to the manu-facturer’s protocol Following bead enrichment, the pooled libraries were sequenced using the GeneReader platform (QIAGEN, Hilden, Germany)
GeneReader data processing
(QIAGEN, Hilden, Germany) was used to QC, align the read data to the hg19 reference genome sequence, call sequence variants, and generate an interactive report for visualization of the sequencing results, as well as a summary of the data QCI Analyze software reports a set of high- and low-confidence variants based on the coverage of variant positions Users have an option to analytically confirm if a variant listed should be valid or invalid before uploading to QCI Interpret software for the clinical interpretation For each sample the report was used to assess the quality of the overall sequencing run and to identify/call the individual variants After review, variants confirmed as analytically valid were uploaded to QCI Interpret for creation of a report for each sample based on detected variants and curated content, with a summary of findings and direct links to evidence sources
Trang 3Illumina MiSeq
The Actionable Insight Tumor Panel (QIAGEN, Hilden,
Germany) was used for a MiSeq (Illumina, CA, USA)
sequencing run The Kapa “with bead” PCR free protocol
(KAPABiosystems, MA, USA) was used in further Illumina
library preparation steps Samples were then paired-end
sequenced on a MiSeq instrument (Illumina, CA, USA)
according to Illumina guidelines The resulting reads were
mapped to the hg19 reference genome sequence using
BWA mem software followed by GATK (best practices) to
recalibrate base quality scores Variants were called using
MuTect Variants were then filtered using GATK (best
practice) and annotated using SnpEff Variants at hotspot
positions were selected using GATK
Pyrosequencing and Sanger analyses
The sample DNA obtained with the QIAamp FFPE
DNA Kit (QIAGEN, Hilden, Germany) was subjected to
Pyrosequencing analysis and Sanger sequencing For
Pyrosequencing the samples were analyzed using the
therascreen RAS Extension Pyro Kit (QIAGEN, Hilden,
Germany) which covers mutations in KRAS codons 59,
61, 117 and 146 as well as NRAS codons 59, 117 and
146 Samples with mutations in KRAS or NRAS codons
12 and 13 were further analyzed with the therascreen
KRAS or NRAS Pyro Kit (QIAGEN, Hilden, Germany)
according to manufacturer’s instructions In addition,
samples that failed the initial PyroMark KRAS analysis
were subjected to a second round of analysis Samples
with an initial“check” status, or with an indicated
muta-tion signal of LOD + 3% (“Potential low level mutamuta-tion”)
were subjected to a second round of analysis performed
in duplicate Sanger sequencing was performed using
Big Dye Terminator Technology and an ABI 3730xl
sequencer (Thermo Fisher Scientific, MA, USA)
Muta-tions were detected by analyzing the sequence trace files
and the quantity of a base at a certain position was
calculated from the area under the curve (AUC) at the
mutation specific position in the electropherogram
Therascreen qPCR
UK) is an allele-specific PCR-based technology with
spe-cific primers for the seven most common KRAS codon
12 and 13 mutations The assay screens for the following
mutations: 12 GCT (Ala), 12 GAT (Asp), 12 CGT (Arg),
12 TGT (Cys), 12 AGT (Ser), 12 GTT (Val), and 13
GAC (Asp) Mutation analysis was performed according
to manufacturer’s instructions, using the RotorGene
real-time PCR instrument (QIAGEN, Hilden, UK)
Ana-lysis of results was performed following the
recommen-dations in the manual, e.g samples with a control assay
with a cycle threshold (Ct) of 35 or higher were deemed
invalid and excluded from the analysis Samples were
called mutation positive based on the delta Ct values reported in the handbook Values over 40 cycles were scored as negative (wild-type)
Results
Evaluation of DNA quality by QuantiMIZE
FFPE samples with ages ranging from 3 to 20 years were used for this study The quality of the extracted DNA was measured by the GeneRead DNA QuantiMIZE QC assay (QIAGEN, Hilden, UK) Thirteen out of 56 samples failed quality checks and were excluded from further analysis (Additional file 1: Table S1) For the remaining 43 sam-ples, 3 to 9 PCR cycles were added (depending on the QuantiMIZE quality scores) to compensate for differences
in DNA quality during enrichment PCR The additional cycles ensured that poor quality (highly fragmented) DNA samples yielded enough material for downstream library preparation The quality of DNA purified from formalin fixated tissue decreases over the sample storage period time [18–20], but also depends on how tissues were treated, handled and processed before and during sample fixation [19, 21, 22]
GeneReader sequencing performance
The QIAact Actionable Insights Tumor Panel (QIAGEN, Hilden, UK) contains 773 unique variant positions in 12 genes (Table 1) An analysis of the reads mapped to the reference showed coverage levels that met the industry-standard 5% sensitivity criteria, even with aged FFPE samples A 200× minimum read coverage cutoff was used for calling a variant at any position in the panel For the 43 FFPE samples analyzed, an average amplicon coverage of 97.2% was observed, and an average variant insight coverage (hotspot coverage) of 99.8% was
samples, an average amplicon coverage of 98.5% was observed and an average variant insight coverage of 99.9% was observed at read depths of ≥200× (Table 1)
Table 1 Parameter and sequencing coverage of Actionable Insight Tumor Panel
Variant allele fraction detection limit 5%
Frequency cut-off and amplicon coverage >500×: 96.4% (A), 92.0% (B)
>200×: 98.5% (A), 97.2% (B) Frequency cut-off and variant insight
coverage
>500×: 99.8% (A), 98.6% (B)
>200×: 99.9% (A), 99.8% (B)
Positive samples included into the study have all been confirmed with Sanger sequencing and passed QuantiMIZE (<0.4) (A) An average of 12 NA12878 samples, (B) average of 43 colorectal cancers FFPE samples (ages 3-20 years)
Trang 4No false negatives (FN; where an expected variant was
not detected) were observed
Performance comparison between the QIAamp and
GeneRead DNA FFPE kits for DNA purification using the
GeneReader
Two DNA purification kits were used to isolate DNA from
FFPE samples Table 2 demonstrates the superior
perform-ance of the GeneRead DNA FFPE Kit (QIAGEN, Hilden,
UK) over the QIAamp DNA FFPE Tissue Kit (QIAGEN,
Hilden, UK) in terms of true positives at lower variant
call-ing sensitivity Fourteen true positive KRAS variants were
detected using an allele fraction cut-off of >5% for DNA
isolated by GeneRead DNA FFPE Kit (QIAGEN, Hilden,
UK) For the QIAamp DNA FFPE Tissue Kit (QIAGEN,
Hilden, UK), 15 KRAS variants were detected using an
allele fraction cut-off of >5% Of the 15 KRAS variants
detected, 14 were true positive variants and 1 was a false
positive (Table 2) as confirmed by several independent
methods Decreasing the allele fraction cutoff to >2.5%
re-sulted in identification of the same 14 KRAS true positive
samples for GeneRead DNA FFPE Kit (QIAGEN, Hilden,
UK) extractions However, for QIAamp DNA FFPE Tissue
Kit (QIAGEN, Hilden, UK) extracted samples at >2.5%
allele fraction cut-off, 11 additional false positive KRAS
mutations (25 variants in total) were detected The
additional mutations were mostly C to T transitions It is
known that FFPE fixation deaminates certain bases, most
prominently cytosine deamination to uracil [23–25] The
GeneRead DNA FFPE Kit (QIAGEN, Hilden, UK) contains
an integrated uracil DNA glycosylase (UDG) step which
removes uracil from the DNA before the final purification
step, yielding high-quality DNA with minimal artifacts
Confirmation of variants by MiSeq, pyrosequencing and
therascreen qPCR assays
The GeneReader NGS System variant calls demonstrated
100% agreement with KRAS mutation status previously
qPCR (Table 3) Of the 43 samples, 14 tested positive for
KRAS variants and 29 samples were confirmed as wild
type The 5% allelic fraction cut-off was used to call
KRAS variants for codons 12, 13, 59, 61, 117 and 146
The true positive variants observed by the GeneReader
NGS System share a 100% concordance with
MiSeq-Illumina (Table 4)
The use of the NA12878 control (Fig 1, Additional file 2: Table S2) and AcroMetrix (Fig 1, Additional file 3: Tables S3) reference standard materials
platform on high frequency and low frequency variants, respectively NA12878 has been used extensively as a ref-erence standard material for verifying NGS platforms [17] and acts as a useful control in establishing background error Besides its use as a GeneReader platform perform-ance standard, AcroMetrix has also been used previously
as a control for variant calls [26]
Discussion
A major advantage of NGS over traditional mutation detection methods is the ability to sequence multiple genes and variants simultaneously Other advantages include minimal DNA input, faster turnaround time;
Table 2 The GeneReader FFPE DNA sample preparation kit
successfully corrects FFPE artifacts
Type of DNA purification kit Allele frequency cut off
Table 3 KRAS agreement study between GeneReader and Pyrosequencing andTherascreen PCR Assays
>5% KRAS variant allele frequency cut off
Pyrosequencing and Therascreen PCR Assays (a)
(a)
If KRAS is mutant by Therascreen KRAS RGQ PCR assay or Therascreen RAS extension Pyrosequencing assay, the condition is recorded as a mutant (MT)
(b)
For Actionable Insights Tumor Panel, a 5% allelic frequency cut off was used
to call variants for codon 12, 13, 59, 61, 117 and 146, which are addressed by established QIAGEN Therascreen PCR assays
Table 4 The concordance study between GeneReader, MiSeq, PyrosequencingandTherascreen PCR assays
Sample no.
KRAS AA change
KRAS variant allele fraction (%) Therascreen PCR/Pyro GeneReadera MiSeqa
+: Variant identified by Therascreen PCR; allele fraction not available
a
: Sample processed from different FFPE section with potentially different
Trang 5lower overall cost and higher throughput and sensitivity
compared to traditional methods [12, 27–29] NGS has
revolutionized the speed of genetic and genomic
discov-ery, and advanced our understanding of the molecular
mechanisms of disease and potential treatment options
However, several major hurdles remain and still prevent
NGS from being broadly adopted in clinical practice
This is especially true for laboratories that are new to this technology, and may lack the in-house expertise required for processing complex bioinformatics data and interpretation of results Such expertise is crucial to construct a bioinformatics pipeline and to evaluate the software and generate quality reports The QIAGEN GeneReader NGS System allows users to perform
Fig 1 Variant calling performances of GeneReader pipeline Each individual data point was generated from 18 data points (a) NA12878 and (b) AcroMetrix Oncology Hotspot
Fig 2 QCI Analyze report showing the alignment of the reads at the variant positions along with the induced amino acid change
Trang 6experiments from sample to insight, tissue sample to
decipherable report based on the interpretation of
sequence variants detected
Analyze’ and ‘QCI Interpret’ for bioinformatics analysis and
reporting of variants, including read mapping, variant
call-ing and interpretation of results It provides visualization of
the alignment of sequencing results (Fig 2) as well as a
summary of the data Quality assessment is also supported,
both at the overall sequencing run level and for the analytic
validity of individual variants to reduce false positive and
negative results Using the data visualization tools within
QCI Analyze, it is possible to determine the quality of the
results and assess any variants of interest Further analysis
of variants using QCI Interpret provides access to the
curated information contained within the QIAGEN
Know-ledge Base enabling a deeper analysis and interpretation of
results for each sample (Fig 3) With all relevant
informa-tion, a report can be created with a summary of findings
and direct links to evidence sources At the single variant
level the QCI software is able to identify an individual
vari-ant as an actionable cancer mutation, and provides links to
current clinical research insights, e.g the KRAS G12D
somatic variant it is established to confer resistance to the
colorectal cancer drugs cetuximab and panitumumab,
based on evidence curated from their FDA drug labels and
clinical practice guidelines Within QCI-Interpret
informa-tion on active clinical trials recruiting colorectal cancer
patients with particular mutations are provided with drug,
nearest location, and trial phase information
The relationship between FFPE DNA quality and se-quencing accuracy is a critical point for any sese-quencing analysis The GeneReader workflow starts with the Gene-Read FFPE DNA Kit for DNA extraction and is specifically designed to reduce artifacts known to commonly occur in FFPE treated samples As seen in Table 2, by using FFPE samples aged from 3 to 20 years, the GeneRead FFPE DNA Kit successfully reduced the number of low quency false positive variants detected These low fre-quency false positive variants are likely caused by cytosine deamination and other fixation associated artifacts Similar phenomena were observed by Bourgon [23], where pre-treatment of FFPE samples with uracil DNA glycosylase (UDG) resulted in a dramatic reduction of false positives, with overall reductions of 77% for C > T and 94% for
G > A changes, respectively Biochemical removal of de-aminated DNA eliminates deamination-associated false positive results; however, for samples with very low quality DNA such as highly fragmented FFPE treated samples, UDG-treated may constitute an issue, as the treatment in-troduces possible further strand breaks leading to even higher fragmentation and lower availability of intact tem-plate strands Therefore, using the QuantiMIZE assay to identify those samples suitable for sequencing, based on
an assessment of original intact and amplifiable templates, before starting an experiment is a critical point for an amplification based NGS technology Previous reports ob-served that samples with lower amounts of amplifiable DNA are more likely to give a markedly increased number
of false positive results [30, 31]
Fig 3 QCI Interpret actionable report, showing summary of findings and link to the insights that can be used to guide clinical research
Trang 7In summary, this study confirms that the GeneReader
NGS System performs consistently and accurately in the
identification of somatic mutations from FFPE samples,
with results confirmed by both alternative technologies
as well as an alternative NGS platform With a full
end-to-end solution with integrated sample preparation and
bioinformatics interpretation, the GeneReader NGS
System is suitable for any laboratory interested in cancer
clinical research
Additional files
Additional file 1: Table S1 The QC results of the extracted DNA
samples were measured using GeneRead DNA QuantiMIZE (DOCX 28 kb)
Additional file 2: Table S2 List of NA12878 Gold Standard Variants
from 18 samples sequenced by GeneReader (DOCX 27 kb)
Additional file 3: Table S3 List of AcroMetrix ™ Oncology Hotspot
Gold Standard Variants from 18 samples sequenced by GeneReader
(DOCX 28 kb) (DOCX 27 kb)
Abbreviations
Ct: Cycle threshold; DNA: Deoxyribonucleic acid; dsDNA: Double-stranded
DNA; FDA: Food and Drug Administration; FFPE: Formalin-fixed
paraffin-embedded; GR: GeneReader; NGS: Next generation sequencing; NSCLC:
Non-small cell lung cancer; PCR: Polymerase chain reaction; qPCR: Quantitative
PCR; QC: Quality control; QCI: QIAGEN clinical insight; TAT: Turn-around time;
UDG: Uracil-DNA glycosylase
Acknowledgements
We greatly appreciate Drs Scott Steelman, Kathleen Steinmann and Robert
Lintner from Broad Technology Labs (Broad Institute of MIT and Harvard, 75
Ames Street (Rm 8021), Cambridge, MA 02141) for their support in samples
processing, GeneReader testing, and manuscript revisions We thanks to Drs.
Vikas Gupta, Naomi Thompson Kiran Divakar and Dietrich Lueerssen from
QIAGEN for the input of the manuscript.
Funding
This work was supported by QIAGEN.
Disclaimer
The sequencing chemistry used in this manuscript is currently available outside
the US The GeneReader NGS System is for research use only An upgraded and
different sequencing chemistry has meanwhile been made available in the US
since April 2017 and will become available worldwide later in 2017 For the
release of the new sequencing chemistry in the US in April, we have shown
equivalency to the sequencing chemistry used in the manuscript.
Availability of data and materials
The analyzed data sets generated during the study are available from the
corresponding author on reasonable request.
Authors ’ contributions
AD, AMH, SS, AS, EL, SH, TR designed the study AD, AMH, SS, AS, DR, MN, TP,
DA, AR, NP, DS, TR performed experiments and analyzed data AD, YK and TR
wrote the manuscript AK, DS, DZ, KK assisted in preparing the manuscript.
All authors read and approved the final manuscript.
Competing interests
At the time of the work was being done the authors where employees of
QIAGEN, and QIAGEN funded the research and the publication costs We
declare that our current or previous employment with QIAGEN did not
influence our interpretation of data or presentation of information.
Consent for publication
Ethics approval and consent to participate FFPE Tumor material from colorectal cancer tumors (Origene Technologies,
MD, USA and Asterand Biosciences, MI, USA) We refer to Origene ’s and Asterand ’s quality system as well as ethics and compliance processes with informed consent for commercial clinical samples We expect Origene and Asterand to follow industry standard ethics approval and consent processes.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Author details
1 QIAGEN Waltham, 35 Gatehouse Dr, Waltham, MA 02451, USA 2 QIAGEN Arhus, Silkeborgvej 2, 8000 Aarhus, Denmark 3 QIAGEN GmbH, QIAGEN Strasse 1, 40724 Hilden, Nordrhein-Westfalen, Germany 4 QIAGEN Redwood City, 1700 Seaport Blvd, Redwood, CA 94063, USA.5QIAGEN Frederick, 6951 Executive Way, Frederick, MD 21703, USA 6 QIAGEN Manchester, Skelton House Lloyd Street North, Manchester M15 6SH, UK 7 Novartis Institutes for BioMedical Research, Cambridge, MA 02139, USA 8 T2 Biosystems, Lexington,
MA 02421, USA.9Macherey-Nigel, Bethlehem, PA 18020, USA.
Received: 28 November 2016 Accepted: 5 May 2017
References
1 Di Marco M, Astolfi A, Grassi E, Vecchiarelli S, Macchini M, Indio V, Casadei R, Ricci C, D ’Ambra M, Taffurelli G, Serra C, Ercolani G, Santini D, D’Errico A, Pinna AD, Minni F, Durante S, Martella LR, Biasco G Characterization of pancreatic ductal adenocarcinoma using whole transcriptome sequencing and copy number analysis by single-nucleotide polymorphism array Mol Med Rep 2015;12:7479 –84.
2 Huang J, Löhr JM, Nilsson M, Segersvärd R, Matsson H, Verbeke C, Heuchel R, Kere J, Iafrate AJ, Zheng Z, Ye W Variant profiling of candidate genes in pancreatic Ductal Adenocarcinoma Clin Chem 2015;61:1408 –16.
3 Gao J, Wu H, Wang L, Zhang H, Duan H, Lu J, Liang Z Validation of targeted next-generation sequencing for RAS mutation detection in FFPE colorectal cancer tissues: comparison with Sanger sequencing and ARMS-scorpion real-time PCR BMJ Open 2016;6:e009532.
4 Sakai K, Yoneshige A, Ito A, Ueda Y, Kondo S, Nobumasa H, Fujita Y, Togashi Y, Terashima M, De Velasco MA, Tomida S, Nishio K Performance of a novel KRAS mutation assay for formalin-fixed paraffin embedded tissues of colorectal cancer Spring 2015;4:7.
5 Sundström M, Edlund K, Lindell M, Glimelius B, Birgisson H, Micke P, Botling J KRAS analysis in colorectal carcinoma: analytical aspects of pyrosequencing and allele-specific PCR in clinical practice BMC Cancer 2010;10:660.
6 Casadio C, Guarize J, Donghi S, Di Tonno C, Fumagalli C, Vacirca D, Dell ’Orto P,
De Marinis F, Spaggiari L, Viale G, Barberis M Molecular testing for targeted therapy in advanced non-small cell lung cancer: suitability of Endobronchial ultrasound Transbronchial needle aspiration Am J Clin Pathol 2015;144:629 –34.
7 Papadopoulou E, Tsoulos N, Tsirigoti A, Apessos A, Agiannitopoulos K, Metaxa-Mariatou V, Zarogoulidis K, Zarogoulidis P, Kasarakis D, Kakolyris S, Dahabreh J, Vlastos F, Zoublios C, Rapti A, Papageorgiou NG, Veldekis D, Gaga M, Aravantinos G, Karavasilis V, Karagiannidis N, Nasioulas G Determination of EGFR and KRAS mutational status in Greek non-small-cell lung cancer patients Oncol Lett 2015;10:2176 –84.
8 Lièvre A, Bachet JB, Boige V, Cayre A, Le Corre D, Buc E, Ychou M, Bouché O, Landi B, Louvet C, André T, Bibeau F, Diebold MD, Rougier P, Ducreux M, Tomasic G, Emile JF, Penault-Llorca F, Laurent-Puig P KRAS mutations as an independent prognostic factor in patients with advanced colorectal cancer treated with cetuximab J Clin Oncol 2008;26:374 –9.
9 Rodriguez R Biomarker testing for treatment of metastatic colorectal cancer: role of the pathologist in community practice J Community Support Oncol 2014;12:27 –32.
10 Martinez DA, Nelson MA The next generation becomes the now generation PLoS Genet 2010;6:e1000906.
11 Sanger F, Nicklen S, Coulson AR DNA sequencing with chain-terminating inhibitors Proc Natl Acad Sci U S A 1997;74:5463 –7.
12 D ’Haene N, Le Mercier M, De Nève N, Blanchard O, Delaunoy M, El Housni H, Dessars B, Heimann P, Remmelink M, Demetter P, Tejpar S, Salmon I Clinical validation of targeted next generation sequencing for Colon and Lung
Trang 813 Le Mercier M, D ’Haene N, De Nève N, Blanchard O, Degand C, Rorive S,
Salmon I Next-generation sequencing improves the diagnosis of thyroid FNA
specimens with indeterminate cytology Histopathology 2015;66:215 –24.
14 Beadling C, Neff TL, Heinrich MC, Rhodes K, Thornton M, Leamon J,
Andersen M, Corless CL Combining highly multiplexed PCR with
semiconductor-based sequencing for rapid cancer genotyping J MolDiagn.
2013;15:171 –6.
15 Metzger GJ, Dankbar SC, Henriksen J, Rizzardi AE, Rosener NK, Schmechel SC.
Development of multigene expression signature maps at the protein level
from digitized immunohistochemistry slides PLoS One 2012;7:e33520.
16 Metzker ML Sequencing technologies - the next generation Nat Rev Genet.
2010;11:31 –46.
17 Zook JM, Chapman B, Wang J, Mittelman D, Hofmann O, Hide W, Salit M.
Integrating human sequence data sets provides a resource of benchmark
SNP and indel genotype calls Nat Biotechnol 2014;32:246 –51.
18 Frickmann H, Tenner-Racz K, Eggert P, Schwarz NG, Poppert S, Tannich E,
Hagen RM Influence of parasite density and sample storage time on the
reliability of Entamoeba Histolytica-specific PCR from formalin-fixed and
paraffin-embedded tissues Diagn Mol Pathol 2013;22:236 –44.
19 Obersteller S, Neubauer H, Hagen RM, Frickmann H Comparison of five
commercial nucleic acid extraction kits for the PCR-based detection of
Burkholderia Pseudomallei DNA in formalin-fixed, paraffin-embedded
tissues Eur J Microbiol Immunol (Bp) 2016;6:244 –52.
20 Kokkat TJ, Patel MS, McGarvey D, VA LV, Baloch ZW Archived formalin-fixed
paraffin-embedded (FFPE) blocks: a valuable underexploited resource for
extraction of DNA, RNA, and protein Biopreserv Biobank 2013;11:101 –6.
21 Chung JY, Braunschweig T, Williams R, Guerrero N, Hoffmann KM, Kwon M,
Song YK, Libutti SK, Hewitt SM Factors in tissue handling and processing
that impact RNA obtained from formalin-fixed, paraffin-embedded tissue.
J Histochem Cytochem 2008;56:1033 –42.
22 Frickmann H, Loderstaedt U, Racz P, Tenner-Racz K, Eggert P, Haeupler A, Bialek R,
Hagen RM Detection of tropical fungi in formalin-fixed, paraffin-embedded
tissue: still an indication for microscopy in times of sequence-based diagnosis?
Biomed Res Int 2015;2015:938721 –32.
23 Bourgon R, Lu S, Yan Y, Lackner MR, Wang W, Weigman V, Wang D, Guan Y,
Ryner L, Koeppen H, Patel R, Hampton GM, Amler LC, Wang Y
High-throughput detection of clinically relevant mutations in archived tumor
samples by multiplexed PCR and next-generation sequencing Clin Cancer
Res 2014;20:2080 –91.
24 Hosein AN, Song S, McCart Reed AE, Jayanthan J, Reid LE, Kutasovic JR,
Cummings MC, Waddell N, Lakhani SR, Chenevix-Trench G, Simpson PT.
Evaluating the repair of DNA derived from formalin-fixed
paraffin-embedded tissues prior to genomic profiling by SNP-CGH analysis Lab
Investig 2013;93:701 –10.
25 Do H, Dobrovic A Dramatic reduction of sequence artefacts from DNA
isolated from formalin-fixed cancer biopsies by treatment with uracil- DNA
glycosylase Oncotarget 2012;3:546 –58.
26 Masucci GV, Cesano A, Hawtin R, Janetzki S, Zhang J, Kirsch I, Dobbin KK,
Alvarez J, Robbins PB, Selvan SR, Streicher HZ, Butterfield LH, Thurin M.
Validation of biomarkers to predict response to immunotherapy in cancer:
volume I - pre-analytical and analytical validation J Immunother Cancer.
2016;4:76 –101.
27 Castéra L, Krieger S, Rousselin A, Legros A, Baumann JJ, Bruet O, Brault B,
Fouillet R, Goardon N, Letac O, Baert-Desurmont S, Tinat J, Bera O, Dugast C,
Berthet P, Polycarpe F, Layet V, Hardouin A, Frébourg T, Vaur D
Next-generation sequencing for the diagnosis of hereditary breast and ovarian
cancer using genomic capture targeting multiple candidate genes Eur J
Hum Genet 2014;22:1305 –13.
28 McCourt CM, McArt DG, Mills K, Catherwood MA, Maxwell P, Waugh DJ,
Hamilton P, O ’Sullivan JM, Salto-Tellez M Validation of next generation
sequencing technologies in comparison to current diagnostic gold
standards for BRAF, EGFR and KRAS mutational analysis PLoS One.
2013;8:e69604.
29 Tuononen K, Mäki-Nevala S, Sarhadi VK, Wirtanen A, Rönty M, Salmenkivi
K, Andrews JM, Telaranta-Keerie AI, Hannula S, Lagström S, Ellonen P,
Knuuttila A, Knuutila S Comparison of targeted next-generation
sequencing (NGS) and real-time PCR in the detection of EGFR, KRAS, and
BRAF mutations on formalin-fixed, paraffin-embedded tumor material of
non-small cell lung carcinoma-superiority of NGS Genes Chromosom
Cancer 2013;52:503 –11.
30 Betge J, Kerr G, Miersch T, Leible S, Erdmann G, Galata CL, Zhan T, Gaiser T, Post S, Ebert MP, Horisberger K, Boutros M Amplicon sequencing of colorectal cancer: variant calling in frozen and formalin-fixed samples PLoS One 2015;10:e0127146.
31 Sah S, Chen L, Houghton J, Kemppainen J, Marko AC, Zeigler R, Latham GJ Functional DNA quantification guides accurate next-generation sequencing mutation detection in formalin-fixed, paraffin-embedded tumor biopsies Genome Med 2013;5:77 –89.
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